feat: swe with any harness

[cmunley1]

anyswe_agent

Update for #1572 to use sandbox api

Runs any Gym agent inside a SWE-style task container and evaluates the resulting
git diff HEAD patch with the dataset harness. Works with hermes_agent,
claude_code_agent, or another compatible Gym agent.

Quickstart

From the repo root, create env.yaml for the policy model server:

policy_base_url: http://localhost:10240/v1
policy_api_key: EMPTY
policy_model_name: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16

Prepare 5 examples (tasks+images):

python responses_api_agents/anyswe_agent/prepare.py --limit 5

Start the environment:

ng_run "+config_paths=[responses_api_agents/anyswe_agent/configs/anyswe_hermes.yaml,responses_api_models/vllm_model/configs/vllm_model.yaml]" \
  ++anyswe_hermes.responses_api_agents.anyswe_agent.container_formatter='responses_api_agents/anyswe_agent/data/sifs/{instance_id}.sif'

Collect rollouts:

ng_collect_rollouts \
  +agent_name=anyswe_hermes \
  +input_jsonl_fpath=responses_api_agents/anyswe_agent/data/swebench_verified.jsonl \
  +output_jsonl_fpath=results/anyswe_rollouts.jsonl \
  +limit=5

Each rollout row contains reward, the full trajectory, and mask_sample for
timeouts or unreliable rewards.

Agent wiring

Point the config at the Gym agent server:

agent_server_module: responses_api_agents.hermes_agent.app
agent_server_class: HermesAgent
agent_config_class: HermesAgentConfig
agent_kwargs: {max_turns: 100, terminal_backend: local}

Agent dependencies install once at startup into a portable prefix mounted inside
the task container. Add setup_scripts/<agent_dir>_deps.sh for new agents.

Dataset and images

prepare.py writes data/swebench_verified.jsonl and builds
data/sifs/{instance_id}.sif.

python responses_api_agents/anyswe_agent/prepare.py

Image builds require apptainer, network access to the SWE-bench registry, and
substantial disk space. Use --limit and --jobs N while iterating. Dataset
prep requires pip install datasets.

Supported datasets: SWE-bench, SWE-bench Multilingual, R2E-Gym.

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[ritaneves]

Assigning @Glorf for the sandbox API

[Glorf]

The overall structure of this implementation looks sound to me. I'd recommend merging it as-is (as a boilerplate) and run the exact accuracy scores alignment with prior implementations/vs the leaderboards as a fast follow-up